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Found 39 papers, 8 papers with code
“It’s our fault!”: Insights Into Users’ Understanding and Interaction With an Explanatory Collaborative Dialog System
We conclude that creating shared mental models between users and AI systems is important to achieving successful dialogs.
AVASAG: A German Sign Language Translation System for Public Services (short paper)
This paper presents an overview of AVASAG; an ongoing applied-research project developing a text-to-sign-language translation system for public services.
Unimodal Multi-Task Fusion for Emotional Mimicry Prediction
In this study, we propose a methodology for the Emotional Mimicry Intensity (EMI) Estimation task within the context of the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild.
The AffectToolbox: Affect Analysis for Everyone
In the field of affective computing, where research continually advances at a rapid pace, the demand for user-friendly tools has become increasingly apparent.
Exploring the Dynamics between Cobot's Production Rhythm, Locus of Control and Emotional State in a Collaborative Assembly Scenario
In industrial scenarios, there is widespread use of collaborative robots (cobots), and growing interest is directed at evaluating and measuring the impact of some characteristics of the cobot on the human factor.
Gaze Detection and Analysis for Initiating Joint Activity in Industrial Human-Robot Collaboration
To the best of our knowledge, this is the first study to analyze the natural gaze behavior of participants working on a joint activity with a robot during a collaborative assembly task.
Fostering User Engagement in the Critical Reflection of Arguments
To support a fair and unbiased opinion-building process, we propose a chatbot system that engages in a deliberative dialogue with a human.
MultiMediate'23: Engagement Estimation and Bodily Behaviour Recognition in Social Interactions
This paper describes the MultiMediate'23 challenge and presents novel sets of annotations for both tasks.
Towards Automated COVID-19 Presence and Severity Classification
COVID-19 presence classification and severity prediction via (3D) thorax computed tomography scans have become important tasks in recent times.
GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions
The effectiveness of the approach was assessed by evaluating its performance in removing identifiable facial attributes to increase the anonymity of the given individual face.
Gaze-based Attention Recognition for Human-Robot Collaboration
We performed an additional validation of our models using the video snippets collected from participants working as an operator in the presented assembly scenario.
ForDigitStress: A multi-modal stress dataset employing a digital job interview scenario
We present a multi-modal stress dataset that uses digital job interviews to induce stress.
GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual Explanations
However, research focusing on counterfactual explanations, specifically for RL agents with visual input, is scarce and does not go beyond identifying defective agents.
Around the world in 60 words: A generative vocabulary test for online research
Six behavioral experiments (N=236) in six countries and eight languages show that (a) our test can distinguish between native speakers of closely related languages, (b) the test is reliable ($r=0. 82$), and (c) performance strongly correlates with existing tests (LexTale) and self-reports.
Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model Ensembles
Because of the overall scarcity of patients with ultra-rare disorders, it is infeasible to directly train a model on them.
Few-Shot Meta Learning for Recognizing Facial Phenotypes of Genetic Disorders
Furthermore, we created simple baselines of few-shot meta-learning methods to improve our base feature descriptor.
Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents
Methods that help users understand the behavior of such agents can roughly be divided into local explanations that analyze specific decisions of the agents and global explanations that convey the general strategy of the agents.
On the Generalizability of ECG-based Stress Detection Models
To the best of our knowledge, this is the first work to compare the cross-dataset generalizability between ECG-based deep learning models and HRV models.
What Do End-Users Really Want? Investigation of Human-Centered XAI for Mobile Health Apps
In healthcare, AI systems support clinicians and patients in diagnosis, treatment, and monitoring, but many systems' poor explainability remains challenging for practical application.
Do We Need Explainable AI in Companies? Investigation of Challenges, Expectations, and Chances from Employees' Perspective
By providing insights into employees' needs and attitudes towards (X)AI, our project report contributes to the development of XAI solutions that meet the requirements of companies and their employees, ultimately driving the successful adoption of AI technologies in the business context.
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era
Speech is the fundamental mode of human communication, and its synthesis has long been a core priority in human-computer interaction research.
An Efficient Multitask Learning Architecture for Affective Vocal Burst Analysis
Affective speech analysis is an ongoing topic of research.
Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems
We evaluate our approach in an extensive user study, revealing that it is able to significantly contribute to the participants' understanding of an AI.
Are 3D Face Shapes Expressive Enough for Recognising Continuous Emotions and Action Unit Intensities?
We also study how 3D face shapes performed on AU intensity estimation on BP4D and DISFA datasets, and report that 3D face features were on par with 2D appearance features in AUs 4, 6, 10, 12, and 25, but not the entire set of AUs.
COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition
In particular, we impose Calibration and Ordinal Ranking constraints on the variance vectors of audiovisual latent distributions.
"GAN I hire you?" -- A System for Personalized Virtual Job Interview Training
This feedback can be helpful concerning the improvement of behavioral skills needed for job interviews.
Employing Socially Interactive Agents for Robotic Neurorehabilitation Training
In today's world, many patients with cognitive impairments and motor dysfunction seek the attention of experts to perform specific conventional therapies to improve their situation.
Alternative Data Augmentation for Industrial Monitoring using Adversarial Learning
However, a precise examination of the resulting images indicate that WGAN and image-to-image translation achieve good segmentation results and only deviate to a small degree from traditional data augmentation.
Intercategorical Label Interpolation for Emotional Face Generation with Conditional Generative Adversarial Networks
This is especially a problem if not only random new images are to be generated, but specific (continuous) features are to be co-modeled.
Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation
As a proof-of-concept, we implement an initial prototype in which the player must traverse a maze that includes several exercise rooms, whereby the generation of the maze is realized by a neural network.
Exploring emotional prototypes in a high dimensional TTS latent space
Recent TTS systems are able to generate prosodically varied and realistic speech.
Do Deep Neural Networks Forget Facial Action Units? -- Exploring the Effects of Transfer Learning in Health Related Facial Expression Recognition
We then fine-tune successively larger parts of this network to learn suitable representations for the task of automatic pain recognition.
Benchmarking Perturbation-based Saliency Maps for Explaining Atari Agents
One of the most prominent methods for explaining the behavior of Deep Reinforcement Learning (DRL) agents is the generation of saliency maps that show how much each pixel attributed to the agents' decision.
GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learning
By doing so, the users of counterfactual explanation systems are equipped with a completely different kind of explanatory information.
Designing a Mobile Social and Vocational Reintegration Assistant for Burn-out Outpatient Treatment
Using Social Agents as health-care assistants or trainers is one focus area of IVA research.
Embedded Emotions -- A Data Driven Approach to Learn Transferable Feature Representations from Raw Speech Input for Emotion Recognition
Traditional approaches to automatic emotion recognition are relying on the application of handcrafted features.
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps
Specifically, we augment strategy summaries that extract important trajectories of states from simulations of the agent with saliency maps which show what information the agent attends to.
Applying Cooperative Machine Learning to Speed Up the Annotation of Social Signals in Large Multi-modal Corpora
Scientific disciplines, such as Behavioural Psychology, Anthropology and recently Social Signal Processing are concerned with the systematic exploration of human behaviour.
Interpreting social cues to generate credible affective reactions of virtual job interviewers
In this paper we describe a mechanism of generating credible affective reactions in a virtual recruiter during an interaction with a user.
